MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships
A dialogue dataset is an indispensable resource for building a dialogue system. Additional information like emotions and interpersonal relationships labeled on conversations enables the system to capture the emotion flow of the participants in the dialogue. However, there is no publicly available Chinese dialogue dataset with emotion and relation labels. In this paper, we collect the conversions from TV series scripts, and annotate emotion and interpersonal relationship labels on each utterance. This dataset contains 25,548 utterances from 4,142 dialogues. We also set up some experiments to observe the effects of the responded utterance on the current utterance, and the correlation between emotion and relation types in emotion and relation classification tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRelation ClassificationSimilar Papers 제목 키워드 기반
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset
Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies are mostly limited to textual modality or two-party dialogue, failing…
Discourse ParsingMulti-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-pa…
Dialogue GenerationResponse GenerationMulti-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-pa…
Dialogue GenerationResponse GenerationAn Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension
In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension. The main goal of this project is to help understand multi-party dialogues. Our d…
Discourse ParsingMachine Reading ComprehensionReading ComprehensionEM Pre-training for Multi-party Dialogue Response Generation
Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialo…
Response Generation